To address some of the challenges posed by traditional ML training, Federated Learning (FL) optimization was introduced in [1]} as a distributed training paradigm that allows keeping the data at its source. Since then, a significant number of FL applications have flourished across different fields, such as medicine [2]}, autonomous driving [3]}, UAV-based wireless networks [4]}. FL has become attractive to foster collaboration among different parties interested in solving a common problem. Under the management of a central server (typically, a neutral entity), FL participants contribute to building a common ML model, by sharing model weights generated using its own local data, rather than forwarding raw data for centralized training.
